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      ["post_title"]=>
      string(292) "NCT04338321 - A Long-term Comparison of Esketamine Nasal Spray Versus Quetiapine Extended Release, Both in Combination With a Selective Serotonin Reuptake Inhibitor/​Serotonin-Norepinephrine Reuptake Inhibitor, in Participants With Treatment Resistant Major Depressive Disorder (ESCAPE-TRD)"
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      string(188) "nct04338321-a-long-term-comparison-of-esketamine-nasal-spray-versus-quetiapine-extended-release-both-in-combination-with-a-selective-serotonin-reuptake-inhibitor-serotonin-norepinephrine-r"
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  ["project_title"]=>
  string(148) "Trajectories of depressive symptom response to esketamine versus quetiapine XR in treatment-resistant depression: A secondary analysis of ESCAPE-TRD"
  ["project_narrative_summary"]=>
  string(785) "We propose a longitudinal group-based trajectory analysis using group-based trajectory modeling of the ESCAPE-TRD data (NCT04338321) to identify latent classes of MADRS response trajectory among patients with TRD during the acute treatment phase (i.e., first 8 weeks), to (i) describe the heterogeneity of response to treatment, (ii) to determine whether treatment allocation predicts response trajectories, and (iii) identify patient characteristics that predict response trajectories. 



Characterizing the latent response trajectories of individuals with TRD receiving quetiapine XR or intranasal esketamine will improve our understanding of the heterogeneity in TRD and the heterogeneity of treatment response occurring with these evidence-based interventions.

"
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  ["principal_investigator"]=>
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    ["first_name"]=>
    string(4) "Greg"
    ["last_name"]=>
    string(4) "Rhee"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(23) "Yale School of Medicine"
    ["email"]=>
    string(19) "taeho.rhee@yale.edu"
    ["state_or_province"]=>
    string(2) "CT"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
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      ["p_pers_f_name"]=>
      string(5) "Jiaqi"
      ["p_pers_l_name"]=>
      string(3) "Liu"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(25) "University of Connecticut"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1584) "Background: Esketamine nasal spray and extended-release (XR) quetiapine are both evidence-based strategies for treatment-resistant depression (TRD). ESCAPE-TRD (NCT04338321) showed esketamine superior for remission at week 8, but group-level estimates obscure clinically important heterogeneity in the pace and pattern of symptom change.



Objective: To identify latent trajectories of symptom change over the 8-week initial treatment phase of ESCAPE-TRD, determine whether treatment allocation predicts trajectory membership, and identify baseline characteristics associated with each trajectory.



Study Design: Secondary analysis of a phase 3b randomized, active-controlled, rater-blinded trial.



Participants: Adults aged 18-74 with TRD (IDS-C30 ≥34; 2-6 failed treatments in the current episode) randomized to esketamine or quetiapine XR alongside a continuing SSRI/SNRI, who received at least one dose.



Primary and Secondary Outcome Measure(s): The primary outcome is latent trajectory class of Montgomery-Asberg Depression Rating Scale (MADRS) score at baseline and weeks 1, 2, 4, 6 and 8. Secondary outcomes are response and remission by visit.



Statistical Analysis: Group-based trajectory modeling (censored normal), with the number of groups and polynomial order selected by the Bayesian information criterion and standard adequacy criteria. Treatment allocation will be entered as a predictor of class membership, and weighted multinomial logistic regression will identify baseline correlates.

"
  ["project_brief_bg"]=>
  string(2916) "Intranasal esketamine and extended-release (XR) quetiapine are both evidence-based augmentation strategies for treatment-resistant depression (TRD). In the ESCAPE-TRD trial, Reif et al. (New England Journal of Medicine, 2023) randomized 676 patients with TRD to esketamine nasal spray or quetiapine XR alongside an ongoing SSRI or SNRI and found esketamine superior, with a higher remission rate at week 8 (27.1% vs. 17.6%; adjusted odds ratio 1.74, 95% CI 1.20-2.52) and a greater least-squares mean reduction in MADRS score at week 8 (between-group difference -2.8 points, 95% CI -4.1 to -1.4).



A limitation of these group-level symptom changes is that they obscure the heterogeneity in how subgroups of patients respond to treatment over time. Two patients ending week 8 with the same MADRS score may have arrived there by very different routes, and a modest average difference may reflect either a small uniform shift across all patients or a substantially better outcome confined to a subgroup. This heterogeneity of response in TRD has been repeatedly demonstrated in both the pharmacotherapy and neurostimulation literature, where between two and six distinct classes have been recovered, typically including a rapid-responding class that separates from the others within the first one to two weeks and, in most studies, one or more non-responding classes (Uher et al., 2010; Smagula et al., 2015; Kaster et al., 2019). It has never been assessed for esketamine, nor in a comparative effectiveness trial comparing esketamine and quetiapine XR.



This work is significant for three reasons. First, identifying a non-responding trajectory that is detectable within the first two weeks would support earlier treatment switching, which is clinically consequential given the in-clinic monitoring burden and cost of esketamine and the metabolic burden of quetiapine XR. Second, establishing whether the advantage of esketamine reflects a redistribution of patients into a rapid-response class, rather than a uniform shift of the whole distribution, materially changes how the ESCAPE-TRD result should be interpreted and communicated to patients.



Characterizing the latent response trajectories of individuals with TRD receiving quetiapine XR or intranasal esketamine will improve our understanding of the heterogeneity in TRD and the heterogeneity of treatment response occurring with these evidence-based interventions. Understanding the trajectories of response for esketamine and quetiapine XR may eventually allow us to personalize treatment selection based on a patient's presentation, which will ultimately improve the care of those suffering from TRD. These analyses require individual participant data with repeated symptom measurement and cannot be conducted from published summary results, which is why access through the YODA Project is being requested.

"
  ["project_specific_aims"]=>
  string(1135) "Using individual participant data from ESCAPE-TRD, we will apply group-based trajectory modeling to accomplish the following aims.



Aim 1: To identify the number and shape of distinct trajectories of depressive symptom change over the 8-week initial treatment phase. We hypothesize that three to six distinct trajectories will be identified, including a non-responding trajectory, a rapid-responding trajectory, and one or more gradual, linear-responding trajectories.



Aim 2: To determine whether randomized treatment allocation predicts trajectory membership. We hypothesize that allocation to esketamine will be associated with greater odds of membership in a rapid-responding trajectory and lower odds of membership in a non-responding trajectory relative to quetiapine XR.



Aim 3: To identify baseline demographic and clinical characteristics associated with trajectory membership. We hypothesize that greater baseline symptom severity, a greater number of prior failed treatments, and longer current episode duration will be associated with the non-responding trajectory.



"
  ["project_study_design"]=>
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    ["label"]=>
    string(25) "Individual trial analysis"
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  ["project_purposes"]=>
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    [0]=>
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      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
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    }
    [2]=>
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      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(69) "confirm_or_validate previously_conducted_research_on_treatment_safety"
      ["label"]=>
      string(69) "Confirm or validate previously conducted research on treatment safety"
    }
    [4]=>
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      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [5]=>
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      ["value"]=>
      string(28) "research_on_comparison_group"
      ["label"]=>
      string(28) "Research on comparison group"
    }
    [6]=>
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      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(1422) "Data source. We are requesting individual participant data from ESCAPE-TRD (NCT04338321) only. We will not pool these data with data from any other study, and no data from studies outside the YODA Project will be used. All analyses will be conducted on individual participant data within the YODA Project secure analytic environment.



Inclusion criteria (applied to ESCAPE-TRD):



1. Randomized in ESCAPE-TRD (full analysis set).



2. Received at least one dose of assigned trial treatment (esketamine nasal spray or quetiapine XR).



3. A baseline MADRS score and at least one post-baseline MADRS score during the 8-week initial treatment phase.



Exclusion criteria: None beyond the above. Participants who discontinue trial treatment during the initial phase will be retained in the primary trajectory model using all observed MADRS assessments; their handling is addressed in the sensitivity analyses described in the Statistical Analysis Plan.



The trial's own eligibility criteria are inherited and define the study sample: adults aged 18-74 years meeting DSM-5 criteria for major depressive disorder, with an IDS-C30 score of 34 or higher, failure of two to six consecutive treatments (including the current treatment) from at least two antidepressant classes in the current episode, and ongoing treatment with an SSRI or SNRI.

"
  ["project_main_outcome_measure"]=>
  string(1104) "Primary outcome. The primary outcome is membership in a latent trajectory class derived from the MADRS total score (range 0-60, higher scores indicating more severe depression) measured at baseline and at weeks 1, 2, 4, 6 and 8. Classes will be characterized by their shape (intercept and slope), their size as a proportion of the analytic sample, and their baseline severity. Trajectory class is derived from the data rather than pre-specified; the number and shape of classes are therefore themselves a study result.



Secondary outcomes. (i) Response, defined as in the parent trial (an improvement of 50% or more in MADRS score from baseline, or a MADRS score of 10 or less), and (ii) remission, defined as a MADRS score of 10 or less, at each study visit through week 8, compared across trajectory classes. Remission defined as a MADRS score of 12 or less will also be reported, to permit comparison with the registrational esketamine trials. 



Any change to these outcome definitions in the final analysis will be reported explicitly in the resulting publication.

"
  ["project_main_predictor_indep"]=>
  string(1023) "The main independent variable is randomized treatment allocation, defined as a two-level categorical variable: esketamine nasal spray plus a continuing SSRI or SNRI versus extended-release quetiapine plus a continuing SSRI or SNRI. Participants will be analyzed according to the group to which they were randomized, irrespective of subsequent discontinuation or dose changes.



Treatment allocation will be tested for an independent effect on trajectory membership in two ways. First, it will be entered directly as a covariate in the multinomial logit function governing group membership within the group-based trajectory model, which tests whether allocation predicts the probability of belonging to each latent class. Second, it will be forced into the weighted multinomial logistic regression model of baseline correlates of class membership, so that the association of allocation with trajectory is estimated adjusting for the baseline characteristics retained by the variable selection procedure.

"
  ["project_other_variables_interest"]=>
  string(1875) "The following baseline variables, all measured before randomization, will be used to describe the analytic sample and as candidate predictors of trajectory membership: age (continuous, and dichotomized as 18-64 vs. 65-74 years, a randomization stratification factor); sex (categorical); body-mass index (continuous); employment status (categorical); geographic region (categorical); number of prior failed treatments in the current episode (2 vs. 3 or more, a randomization stratification factor); age at diagnosis of major depressive disorder (continuous); duration of the current depressive episode (continuous, in weeks, log-transformed given its skewed distribution); total number of lifetime depressive episodes (continuous); baseline MADRS total score (continuous); baseline IDS-C30 total score (continuous); baseline CGI-S score (ordinal); and class of the continuing antidepressant (SSRI vs. SNRI).



Where captured in the trial database, we will also consider prior quetiapine use requiring washout at screening, concomitant benzodiazepine or hypnotic use (binary), psychiatric comorbidity (binary), and psychotropic medications (binary). Availability and completeness of each variable will be confirmed against the data dictionary on receipt of the dataset, and any variable that is unavailable or has excessive missingness will be dropped and reported as such.



Variables measuring the same underlying construct will not be entered into the same model (for example, baseline MADRS and baseline IDS-C30 will be assessed for collinearity and one retained). Multicollinearity will be assessed using the variance inflation factor, with a threshold of 4. Multilevel categorical variables with sparse cells will be collapsed where clinically sensible, and excluded if the expected frequency within any trajectory class is fewer than 5.

"
  ["project_stat_analysis_plan"]=>
  string(4632) "All analyses will be conducted within the YODA Project secure analytic environment. Trajectory models will be fitted in R (lcmm) or Stata (traj) and the software reported.



1. Descriptive analyses. Baseline characteristics will be summarized overall, by arm and by trajectory class, using means and standard deviations, medians and interquartile ranges, or counts and percentages, with between-class comparisons by ANOVA, Kruskal-Wallis or chi-square tests.



2. Trajectory modeling (Aim 1). Following Kaster et al. (2019), MADRS score will be modeled using a censored normal distribution with censoring at the scale limits (0 and 60), with time modeled as the actual study week (0, 1, 2, 4, 6, 8) to preserve the unequal spacing of assessments. We will first fit one- through six-group models with the maximum polynomial order fixed at cubic, selecting the number of groups by the Bayesian information criterion (BIC) and the log Bayes factor approximation (2 x delta-BIC), with a value above 10 favoring the more complex model. Holding the number of groups fixed, we will then reduce the polynomial order of the trajectory with the smallest non-significant higher-order term until all are linear, retaining the combination with the lowest BIC. Adequacy will be assessed by the average posterior probability of membership (minimum 0.70 per group), the proportion of the sample per group (minimum 5%), and the odds of correct classification (greater than 5); entropy will also be reported. Adequacy statistics for all candidate models will be tabulated, and reporting will follow the GRoLTS checklist.



3. Treatment allocation (Aim 2). Allocation will be entered as a covariate in the multinomial logit function for group membership within the trajectory model, estimating whether it predicts class membership. Secondarily, models will be fitted separately within each arm and the resulting class shapes and sizes compared qualitatively, recognizing that smaller within-arm samples may support fewer classes.



4. Baseline correlates (Aim 3). Associations between baseline characteristics and trajectory membership will be estimated using multinomial logistic regression weighted by the posterior probability of membership, to account for uncertainty in class assignment. The reference class will be specified a priori as the largest class. Covariates will be screened as described under Other Variables of Interest, then selected by deriving 1,000 bootstrap samples, applying backward stepwise elimination at p < 0.05 within each, and retaining covariates selected in more than 75% of samples; allocation will be forced into every bootstrapped model. Retained covariates will enter a final multinomial model, reported as odds ratios with 95% confidence intervals. Discrimination and calibration will be assessed by class using the c-statistic and Hosmer-Lemeshow test.



5. Response and remission by class (secondary). Response and remission rates will be compared across classes at each visit using chi-square tests, with alpha set to 0.01 for multiple comparisons; cells with expected counts below 5 will not be tested.



6. Missing data and sensitivity analyses. The primary model uses all observed MADRS assessments, estimated by maximum likelihood, accommodating intermittent missingness under a missing-at-random assumption. Differential discontinuation of trial treatment (23.2% with esketamine vs. 40.3% with quetiapine XR) is the principal threat to validity and will be addressed by five pre-specified sensitivity analyses: (a) restricting the model to participants with three or more post-baseline MADRS assessments; (b) repeating the model after applying the trial's nonresponder imputation and last-observation-carried-forward conventions, to relate results to the published analysis; (c) comparing rates and timing of discontinuation across classes and arms, treating discontinuation as an outcome; (d) deriving class assignment from data through week 4 and comparing it with week-8 assignment using Cohen's kappa; and (e) a pattern-mixture analysis assuming no further improvement after the last observation among those who discontinue.



7. Sample size. The sample is fixed at the 676 randomized participants, so no power calculation is performed. For reference, the prior application of this approach recovered four well-separated classes with 388 participants; a class at the 5% minimum here would contain roughly 34 participants. Tests will be two-tailed with alpha 0.05 unless specified otherwise.

"
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      ["label"]=>
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      ["label"]=>
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    }
    [2]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1011) "The timeline below assumes approval of this request and execution of the Data Use Agreement, and fits within the 12-month access period.



Project start (data access): within 4 weeks of Data Use Agreement execution; anticipated November 2026.



Data familiarization, data dictionary review and cohort construction: November 2026 to January 2027.



Primary trajectory modeling, regression analyses and sensitivity analyses complete: April 2027.



Manuscript drafted and circulated to co-authors: June 2027.



Manuscript first submitted for publication: July 2027.



Results reported back to the YODA Project: at the time of first submission, with the final publication or a summary of findings provided when available.



If additional time is required, for example because of unanticipated data structure issues, an extension of the access period will be requested before the initial 12-month period ends.



"
  ["project_dissemination_plan"]=>
  string(1295) "The primary product will be a single peer-reviewed manuscript reporting the trajectory analysis, the association of treatment allocation with trajectory membership, and the baseline characteristics associated with each trajectory. Suitable journals include the American Journal of Psychiatry, JAMA Psychiatry, the British Journal of Psychiatry, or Neuropsychopharmacology.



We also anticipate presenting the findings as abstracts or posters at scientific meetings, including the American College of Neuropsychopharmacology, the Society of Biological Psychiatry, the European College of Neuropsychopharmacology and the Canadian Psychiatric Association annual meetings.



The target audiences are clinicians who treat treatment-resistant depression, clinical trialists and methodologists working on heterogeneity of treatment response, and guideline developers. A plain-language summary will accompany the manuscript for patient and public audiences.



Analytic code will be made publicly available on submission to support reproducibility. In accordance with YODA Project policy, findings will be reported and submitted for publication regardless of the direction of the results, and the YODA Project will be acknowledged as the source of the data.

"
  ["project_bibliography"]=>
  string(2461) "
Jones, B.L., Nagin, D.S., Roeder, K., 2001. A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research 29, 374-393.
Kass, R.E., Raftery, A.E., 1995. Bayes factors. Journal of the American Statistical Association 90, 773-795.
Kaster, T.S., Downar, J., Vila-Rodriguez, F., Thorpe, K.E., Feffer, K., Noda, Y., Giacobbe, P., Knyahnytska, Y., Kennedy, S.H., Lam, R.W., Daskalakis, Z.J., Blumberger, D.M., 2019. Trajectories of response to dorsolateral prefrontal rTMS in major depression: a THREE-D study. American Journal of Psychiatry 176, 367-375.
Montgomery, S.A., Asberg, M., 1979. A new depression scale designed to be sensitive to change. British Journal of Psychiatry 134, 382-389.
Nagin, D.S., Odgers, C.L., 2010. Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology 6, 109-138.
van de Schoot, R., Sijbrandij, M., Winter, S.D., Depaoli, S., Vermunt, J.K., 2017. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Structural Equation Modeling 24, 451-467.
Reif, A., Bitter, I., Buyze, J., Cebulla, K., Frey, R., Fu, D.-J., Ito, T., Kambarov, Y., Llorca, P.-M., Oliveira-Maia, A.J., Messer, T., Mulhern-Haughey, S., Rive, B., von Holt, C., Young, A.H., Godinov, Y., 2023. Esketamine nasal spray versus quetiapine for treatment-resistant depression. New England Journal of Medicine 389, 1298-1309.
Rush, A.J., Trivedi, M.H., Wisniewski, S.R., Nierenberg, A.A., Stewart, J.W., Warden, D., Niederehe, G., Thase, M.E., Lavori, P.W., Lebowitz, B.D., McGrath, P.J., Rosenbaum, J.F., Sackeim, H.A., Kupfer, D.J., Luther, J., Fava, M., 2006. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. American Journal of Psychiatry 163, 1905-1917.
Smagula, S.F., Butters, M.A., Anderson, S.J., Lenze, E.J., Dew, M.A., Mulsant, B.H., Lotrich, F.E., Aizenstein, H., Reynolds, C.F., 2015. Antidepressant response trajectories and associated clinical prognostic factors among older adults. JAMA Psychiatry 72, 1021-1028.
Uher, R., Muthen, B., Souery, D., Mors, O., Jaracz, J., Placentino, A., Petrovic, A., Zobel, A., Henigsberg, N., Rietschel, M., Aitchison, K.J., Farmer, A., McGuffin, P., 2010. Trajectories of change in depression severity during treatment with antidepressants. Psychological Medicine 40, 1367-1377.
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  array(0) {
  }
  ["project_due_dil_assessment"]=>
  array(21) {
    ["ID"]=>
    int(20284)
    ["id"]=>
    int(20284)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2026-0832"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2026-0832.pdf"
    ["filesize"]=>
    int(108123)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2026/08/YODA-Project-Due-Diligence-Assessment-2026-0832.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2026-0832/yoda-project-due-diligence-assessment-2026-0832/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2026-0832"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19926)
    ["date"]=>
    string(19) "2026-10-20 20:39:48"
    ["modified"]=>
    string(19) "2026-10-20 20:39:48"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(20285)
    ["id"]=>
    int(20285)
    ["title"]=>
    string(46) "YODA Project Protocol - 2026-0832 - 2026-09-03"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2026-0832-2026-09-03.pdf"
    ["filesize"]=>
    int(133711)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2026/08/YODA-Project-Protocol-2026-0832-2026-09-03.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2026-0832/yoda-project-protocol-2026-0832-2026-09-03/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2026-0832-2026-09-03"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19926)
    ["date"]=>
    string(19) "2026-10-20 20:40:04"
    ["modified"]=>
    string(19) "2026-10-20 20:40:04"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(20286)
    ["id"]=>
    int(20286)
    ["title"]=>
    string(36) "YODA Project Review - 2026-0832_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2026-0832_site.pdf"
    ["filesize"]=>
    int(1321249)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2026/08/YODA-Project-Review-2026-0832_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2026-0832/yoda-project-review-2026-0832_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
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    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(19926)
    ["date"]=>
    string(19) "2026-10-20 20:40:34"
    ["modified"]=>
    string(19) "2026-10-20 20:40:34"
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    string(3) "pdf"
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  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(1) {
  [0]=>
  string(13) "United States"
}


pi affil
array(1) {
  [0]=>
  string(8) "Academia"
}


products
array(0) {
}


num of trials
array(1) {
  [0]=>
  string(1) "0"
}


res
array(1) {
  [0]=>
  string(1) "3"
}